diff --git a/examples/fabric/kfold_cv/README.md b/examples/fabric/kfold_cv/README.md
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+## K-Fold Cross Validation
+
+This is an example of performing K-Fold cross validation supported with [Lightning Fabric](https://pytorch-lightning.readthedocs.io/en/stable/fabric/fabric.html). To learn more about cross validation, check out [this article](https://sebastianraschka.com/blog/2016/model-evaluation-selection-part3.html#introduction-to-k-fold-cross-validation).
+
+We use the MNIST dataset to train a simple CNN model. We create the k-fold cross validation splits using the `ModelSelection.KFold` [class](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html) in the `scikit-learn` library. Ensure that you have the `scikit-learn` library installed;
+
+```bash
+pip install scikit-learn
+```
+
+#### Run K-Fold Image Classification with Lightning Fabric
+
+This script shows you how to scale the pure PyTorch code to enable GPU and multi-GPU training using [Lightning Fabric](https://pytorch-lightning.readthedocs.io/en/stable/fabric/fabric.html).
+
+```bash
+# CPU
+lightning run model train_fabric.py
+
+# GPU (CUDA or M1 Mac)
+lightning run model train_fabric.py --accelerator=gpu
+
+# Multiple GPUs
+lightning run model train_fabric.py --accelerator=gpu --devices=4
+```
+
+### References
+
+- [KFold Model Selection](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html)
+- [K-Fold Cross Validation by Sebastian Rashcka](https://sebastianraschka.com/blog/2016/model-evaluation-selection-part3.html#introduction-to-k-fold-cross-validation)
+- [Cross Validation Wiki](<https://en.wikipedia.org/wiki/Cross-validation_(statistics)#k-fold_cross-validation>)
diff --git a/examples/fabric/kfold_cv/train_fabric.py b/examples/fabric/kfold_cv/train_fabric.py
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+# Copyright The Lightning AI team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import argparse
+from os import path
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+import torchvision.transforms as T
+from sklearn import model_selection
+from torch.utils.data import DataLoader, SubsetRandomSampler
+from torchmetrics.classification import Accuracy
+from torchvision.datasets import MNIST
+
+from lightning.fabric import Fabric  # import Fabric
+from lightning.fabric import seed_everything
+
+DATASETS_PATH = path.join(path.dirname(__file__), "..", "..", "..", "Datasets")
+
+
+class Net(nn.Module):
+    def __init__(self) -> None:
+        super().__init__()
+        self.conv1 = nn.Conv2d(1, 32, 3, 1)
+        self.conv2 = nn.Conv2d(32, 64, 3, 1)
+        self.dropout1 = nn.Dropout(0.25)
+        self.dropout2 = nn.Dropout(0.5)
+        self.fc1 = nn.Linear(9216, 128)
+        self.fc2 = nn.Linear(128, 10)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = F.relu(x)
+        x = self.conv2(x)
+        x = F.relu(x)
+        x = F.max_pool2d(x, 2)
+        x = self.dropout1(x)
+        x = torch.flatten(x, 1)
+        x = self.fc1(x)
+        x = F.relu(x)
+        x = self.dropout2(x)
+        x = self.fc2(x)
+        output = F.log_softmax(x, dim=1)
+        return output
+
+
+def train_dataloader(model, data_loader, optimizer, fabric, epoch, hparams, fold):
+    # TRAINING LOOP
+    model.train()
+    for batch_idx, (data, target) in enumerate(data_loader):
+        # NOTE: no need to call `.to(device)` on the data, target
+        optimizer.zero_grad()
+        output = model(data)
+        loss = F.nll_loss(output, target)
+        fabric.backward(loss)  # instead of loss.backward()
+
+        optimizer.step()
+        if (batch_idx == 0) or ((batch_idx + 1) % hparams.log_interval == 0):
+            print(
+                "Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}".format(
+                    epoch,
+                    batch_idx * len(data),
+                    len(data_loader.dataset),
+                    100.0 * batch_idx / len(data_loader),
+                    loss.item(),
+                )
+            )
+
+        if hparams.dry_run:
+            break
+
+
+def validate_dataloader(model, data_loader, fabric, hparams, fold, acc_metric):
+    model.eval()
+    loss = 0
+    with torch.no_grad():
+        for data, target in data_loader:
+            # NOTE: no need to call `.to(device)` on the data, target
+            output = model(data)
+            loss += F.nll_loss(output, target, reduction="sum").item()
+
+            # Accuracy with torchmetrics
+            acc_metric.update(output, target)
+
+            if hparams.dry_run:
+                break
+
+    # all_gather is used to aggregated the value across processes
+    loss = fabric.all_gather(loss).sum() / len(data_loader.dataset)
+
+    # compute acc
+    acc = acc_metric.compute() * 100
+
+    print(f"\nFor fold: {fold} Validation set: Average loss: {loss:.4f}, Accuracy: ({acc:.0f}%)\n")
+    return acc
+
+
+def run(hparams):
+    # Create the Lightning Fabric object. The parameters like accelerator, strategy, devices etc. will be proided
+    # by the command line. See all options: `lightning run model --help`
+    fabric = Fabric()
+
+    seed_everything(hparams.seed)  # instead of torch.manual_seed(...)
+
+    transform = T.Compose([T.ToTensor(), T.Normalize((0.1307,), (0.3081,))])
+    # This is meant to ensure the data are download only by 1 process.
+    if fabric.is_global_zero:
+        MNIST(DATASETS_PATH, download=True)
+    fabric.barrier()
+
+    # initialize dataset
+    dataset = MNIST(DATASETS_PATH, train=True, transform=transform)
+
+    # Loop over different folds (shuffle = False by default so reproducible)
+    folds = hparams.folds
+    kfold = model_selection.KFold(n_splits=folds)
+
+    # initialize n_splits models and optimizers
+    models = [Net() for _ in range(kfold.n_splits)]
+    optimizers = [optim.Adadelta(model.parameters(), lr=hparams.lr) for model in models]
+
+    # fabric setup for models and optimizers
+    for i in range(kfold.n_splits):
+        models[i], optimizers[i] = fabric.setup(models[i], optimizers[i])
+
+    # Accuracy using torchmetrics
+    acc_metric = Accuracy(task="multiclass", num_classes=10).to(fabric.device)
+
+    # loop over epochs
+    for epoch in range(1, hparams.epochs + 1):
+
+        # loop over folds
+        epoch_acc = 0
+        for fold, (train_ids, val_ids) in enumerate(kfold.split(dataset)):
+            print(f"Working on fold {fold}")
+
+            # initialize dataloaders based on folds
+            batch_size = hparams.batch_size
+            train_loader = DataLoader(dataset, batch_size=batch_size, sampler=SubsetRandomSampler(train_ids))
+            val_loader = DataLoader(dataset, batch_size=batch_size, sampler=SubsetRandomSampler(val_ids))
+
+            # get model and optimizer for the current fold
+            model, optimizer = models[fold], optimizers[fold]
+
+            # train and validate
+            train_dataloader(model, train_loader, optimizer, fabric, epoch, hparams, fold)
+            epoch_acc += validate_dataloader(model, val_loader, fabric, hparams, fold, acc_metric)
+            acc_metric.reset()
+
+        # log epoch metrics
+        print(f"Epoch {epoch} - Average acc: {epoch_acc / kfold.n_splits}")
+
+        if hparams.dry_run:
+            break
+
+    # When using distributed training, use `fabric.save`
+    # to ensure the current process is allowed to save a checkpoint
+    if hparams.save_model:
+        fabric.save(model.state_dict(), "mnist_cnn.pt")
+
+
+if __name__ == "__main__":
+    # Arguments can be passed in through the CLI as normal and will be parsed here
+    # Example:
+    # lightning run model image_classifier.py accelerator=cuda --epochs=3
+    parser = argparse.ArgumentParser(description="Fabric MNIST K-Fold Cross Validation Example")
+    parser.add_argument(
+        "--batch-size", type=int, default=64, metavar="N", help="input batch size for training (default: 64)"
+    )
+    parser.add_argument("--epochs", type=int, default=14, metavar="N", help="number of epochs to train (default: 14)")
+    parser.add_argument("--lr", type=float, default=1.0, metavar="LR", help="learning rate (default: 1.0)")
+    parser.add_argument("--gamma", type=float, default=0.7, metavar="M", help="Learning rate step gamma (default: 0.7)")
+    parser.add_argument("--dry-run", action="store_true", default=False, help="quickly check a single pass")
+    parser.add_argument("--seed", type=int, default=1, metavar="S", help="random seed (default: 1)")
+    parser.add_argument(
+        "--log-interval",
+        type=int,
+        default=10,
+        metavar="N",
+        help="how many batches to wait before logging training status",
+    )
+    parser.add_argument("--folds", type=int, default=5, help="number of folds for k-fold cross validation")
+    parser.add_argument("--save-model", action="store_true", default=False, help="For Saving the current Model")
+    hparams = parser.parse_args()
+
+    run(hparams)