Cross-validation is a statistical method used to assess the performance of machine learning models by partitioning data into subsets and evaluating the model’s effectiveness on each subset. This technique helps identify issues such as overfitting and selection bias, offering insights into how well the model is likely to generalize to unseen data. Cross-validation is particularly beneficial for data scientists and machine learning practitioners seeking to validate and enhance the predictive accuracy of their models.
