Cross-validation is a statistical method used in machine learning to evaluate the performance of a model by splitting data into multiple subsets, training the model on some subsets, and testing it on others. The most common type is k-fold cross-validation. This technique helps prevent overfitting and ensures that the model generalizes well to unseen data.