What is cross-validation in supervised learning?

Cross-validation evaluates models by splitting data into multiple train-test folds.

K-fold cross-validation divides data into K subsets. Each fold is used as a test set once while the rest are used for training. This reduces variance in evaluation and provides a more reliable estimate of model performance.