Cross-validation

Statistical method used to estimate the skill of machine learning models by partitioning the data into subsets and evaluating the model's performance on each subset. Cross-validation helps in identifying issues like overfitting and selection bias, providing insights into how well the model will generalize to an independent dataset. It is primarily beneficial for data scientists and machine learning practitioners aiming to validate and improve their models' predictive accuracy.