Cross-validation is a cornerstone technique in machine learning that ensures models are robust, generalize well, and avoid overfitting. This blog explores its fundamental concepts, various methods, and practical implementation, empowering data scientists to build more trustworthy AI systems. From k-fold to stratified approaches, discover how cross-validation enhances model evaluation and accelerates innovation in the AI landscape.