Model selection criteria are essential tools in statistical modeling and machine learning that help identify the best model among a set of candidates by balancing goodness of fit and model complexity. These criteria aim to prevent overfitting and ensure the model's generalizability to new data by incorporating penalty terms for the number of parameters used.