Model selection is the process of choosing the best statistical or machine learning model for a given dataset. This involves evaluating different models based on their performance metrics, such as accuracy, precision, and recall. The goal is to find a model that generalizes well to new, unseen data while avoiding overfitting, which occurs when a model learns noise instead of the underlying pattern. Various techniques can assist in model selection, including cross-validation, grid search, and information criteria like AIC or BIC. These methods help compare models systematically, ensuring that the chosen model is both effective and efficient for the specific problem at hand.