Cross-validation

In the realm of statistics, cross-validation is a technique used to validate the effectiveness of a model's predictive capabilities. It involves partitioning a sample of data into complementary subsets, performing the analysis on one subset, and validating the analysis on the other subset. This method helps to mitigate issues like overfitting, and provides insight into how the model will generalize to an independent dataset.