I explained that I evaluated the model's performance using metrics like accuracy, precision, recall, and F1-score, depending on the problem type. For regression models, I used metrics such as MSE and R-squared. I also mentioned the importance of cross-validation to ensure the model's robustness and avoid overfitting.