Cross-validation is a technique used to assess the performance of a machine learning model by splitting the data into multiple subsets (folds). The model is trained on a combination of these folds and tested on the remaining fold. This process is repeated for each fold, and the model's performance is averaged across all iterations. Cross-validation helps to ensure that the model performs well on unseen data and is not overfitting or underfitting.