What is the bias-variance tradeoff curve and how is it used in model selection?

The bias-variance curve shows how total error changes with model complexity, helping select the optimal model that minimizes validation error.

The bias-variance tradeoff curve plots training error, validation error, bias, and variance against model complexity. As complexity increases, bias decreases while variance increases. The validation error typically forms a U-shape, and the optimal model is chosen at its minimum. This curve is central to hyperparameter tuning and model selection in machine learning pipelines.