Also known as: Cross validation
Cross-validation is an evaluation method that estimates how well a model generalizes by training and testing it on different non-overlapping subsets of the data. It helps reduce dependence on a single train-test split and can provide more reliable performance estimates.
A mechanism for estimating how well a model would generalize to new data by testing the model against one or more non-overlapping data subsets withheld from the training set.