Model validation courses can help you learn statistical techniques, performance metrics, overfitting prevention, and cross-validation methods. You can build skills in assessing model accuracy, understanding bias-variance tradeoffs, and implementing robust validation frameworks. Many courses introduce tools like R, Python, and specialized libraries such as scikit-learn and TensorFlow, that support applying these validation techniques to both traditional statistical models and those used in artificial intelligence (AI) applications.