Model training involves fitting the model to the labeled training data, updating its parameters based on the observed examples to minimize the prediction errors. Model evaluation, on the other hand, assesses how well the trained model performs on new, unseen data. This step is essential to understand how the model generalizes to real-world scenarios and aids in selecting the best model for deployment.