Huber loss is a combination of mean squared error (MSE) and mean absolute error (MAE). It behaves like MSE for small errors and like MAE for large errors. This makes it more robust to outliers compared to MSE, which can be sensitive to extreme values. Huber loss transitions smoothly between quadratic and linear loss functions, controlled by a parameter called delta. By adjusting delta, you can control the balance between the sensitivity to small errors and the robustness to outliers.