Cross-validation is a cornerstone technique in machine learning that ensures models generalize well to unseen data. This blog dives deep into the concept of cross-validation, exploring its types, benefits, and practical implementation. From k-fold to stratified and leave-one-out methods, we unravel how these strategies help mitigate overfitting and provide reliable performance estimates. With clear explanations and Python code snippets, this guide equips data scientists and AI enthusiasts with the tools to build more accurate and trustworthy models.