K-Fold and Stratified Cross-Validation are two useful methods for checking how well a model performs. But they work a bit differently. Let’s break them down:

1. K-Fold Cross-Validation:

• This method divides the data into k equal pieces, which are called folds.
• Each piece gets a turn to be the validation set.
• While one fold is used to test the model, the other folds are used to train it.

2. Stratified Cross-Validation:

• This method makes sure that each fold has the same mix of different classes as the whole dataset.
• It's especially helpful when dealing with imbalanced data, meaning some classes have a lot more examples than others.
• This helps keep the original balance of the data.

In summary, K-Fold is simpler to understand, while Stratified Cross-Validation is better for keeping different classes represented fairly!