What is the purpose of cross-validation in machine learning?

Cross-validation is used to assess the generalizability of a model by splitting the data into several subsets (folds) and training and testing the model on different combinations of these subsets. The most common method is k-fold cross-validation, which helps in avoiding overfitting and provides a better estimate of model performance on unseen data.