A confusion matrix is a tool used to evaluate classification models on test data. It provides a breakdown of correct and incorrect predictions made by the model compared to actual classifications. The matrix has rows for predicted classifications and columns for actual classifications. It allows calculating important metrics like accuracy, precision, recall, and F1 score to assess model performance.