Model evaluation is the systematic process of assessing machine learning model performance using quantitative metrics, validation strategies, and diagnostic techniques. It bridges the gap between training and deployment by answering whether a model generalizes well to unseen data rather than merely memorizing training patterns. The fundamental tension in evaluation is the bias-variance tradeoff: models must be complex enough to capture real patterns but simple enough to avoid fitting noise, and proper evaluation separates good models from dangerously overconfident ones.