Cross-validation is a statistical method used to estimate the skill of machine learning models by partitioning the data into subsets, training the model on some subsets, and validating it on the remaining ones. This technique helps in assessing how the results of a statistical analysis will generalize to an independent data set, thereby preventing overfitting and ensuring model robustness.