Model evaluation is a crucial step in the machine learning pipeline that involves assessing the performance of a predictive model using specific metrics to ensure its accuracy and generalizability. It helps in understanding the model's strengths and weaknesses, guiding improvements and ensuring that the model meets the desired objectives before deployment.