How does cross-validation improve model reliability?

Cross-validation provides more reliable performance estimates by testing on multiple data splits.

Instead of relying on a single train-test split, cross-validation divides data into k folds. The model is trained k times, each time using a different fold as validation. This reduces variance in evaluation metrics and ensures robustness across different data distributions.