Model validation is the process of checking and testing a predictive model for its accuracy, reliability, and efficacy before it goes into practical usage. The model will undergo rigorous testing with different data sets to make sure that it does what was expected: produce accurate predictions.
It helps determine biases, mistakes, and problems in the model so that it becomes accurate and trustworthy. This is a very important process in data science and machine learning, where models help make key decisions based on data.
