Cross validation is a statistical method used to evaluate the performance of a model by partitioning the data into subsets, training the model on some subsets, and testing it on the remaining subsets. This technique ensures that the model's performance is not overly dependent on a single partition of the data, thus providing a more robust measure of its predictive power.
