Cross-validation is a statistical method used to evaluate the effectiveness and predictive power of a model by partitioning the data into subsets. One subset is used to train the model, and the rest for testing. This process is repeated multiple times, each time using a different subset as the test set. It's vital for avoiding overfitting, ensuring the model performs well on unseen data.