K-Fold
A cross-validation technique where the dataset is divided into 'k' equal-sized subsets, and the model is trained and evaluated 'k' times iteratively.
Think of It Like This
Like a sports team playing every other team in the league once to ensure their overall ranking isn't just a fluke based on one lucky game.
In each iteration, one fold is used as the validation set while the remaining 'k-1' folds are used for training. The final performance metric is the average across all 'k' trials. This provides a much more robust and unbiased estimate of a model's generalization capabilities compared to a single train-test split.