Evaluation metrics are tools used to assess the performance of models, particularly in fields like machine learning and data science. They provide quantitative measures that help determine how well a model is making predictions or classifications. Common metrics include accuracy, precision, recall, and F1 score, each serving a specific purpose depending on the problem being addressed. These metrics allow practitioners to compare different models and choose the best one for their specific task. By analyzing these values, one can identify strengths and weaknesses in a model's performance, guiding improvements and ensuring that the model meets the desired objectives effectively.