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Cross-Validation is a book category focused on the theory and practice of evaluating and selecting predictive models, with an emphasis on estimating out-of-sample performance and avoiding overfitting. Books in this category cover resampling schemes (k-fold, leave-one-out, stratified and repeated CV, bootstrap, nested CV), adaptations for time series and imbalanced data, and the use of performance metrics, uncertainty estimation, and learning curves. They address bias–variance trade-offs, data splitting strategies, common pitfalls like leakage and optimistic bias, and best practices for reproducible experiments and fair benchmarking. The category spans introductory texts, advanced research monographs, practical handbooks tied to tools such as scikit-learn, R caret, and tidymodels, and domain-specific case studies in areas like NLP, computer vision, finance, and bioinformatics. Aimed at students, data scientists, and researchers, these books provide both methodological foundations and step-by-step guidance for robust model assessment and selection.

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