Evaluating model performance post-compilation is crucial for machine learning success. Assessing generalization with validation datasets, monitoring accuracy, precision, recall, and F1 score are essential. Tools like confusion matrices and learning curves reveal overfitting or underfitting. Continuous monitoring and hyperparameter tuning enhance model effectiveness.