In this video, I explain cross-validation, a technique to evaluate and improve a model’s performance. Cross-validation involves splitting the dataset into multiple subsets (or "folds"). The model is trained on some of these subsets while being tested on the remaining one, and this process is repeated several times. The most common form is k-fold cross-validation, where the data is divided into k-folds. Each fold is a test set once, and the results are averaged to give a more reliable estimate of the model's performance.