Cross-validation is a statistical technique used in mathematical statistics and machine learning to assess the performance and generalization ability of a predictive model. It involves partitioning a dataset into multiple subsets, training the model on some of these subsets, and then evaluating its performance on the remaining data . The primary goal of cross-validation is to estimate how well the model will perform on unseen data, which helps in model selection and hyperparameter tuning. Cross-validation helps in model selection by comparing the performance of different models on the same dataset. It also aids in hyperparameter tuning by assessing how different hyperparameters affect model performance. By using cross-validation, researchers and data scientists can make more informed decisions about which models and parameter settings are likely to perform well when applied to new, unseen data, thereby improving the reliability of statistical analyses and predictions.