Overfitting

The dots are training data; the curve is the model. A simple curve captures the real trend but misses some points, so training error stays a little high. Crank up complexity and the curve bends through every dot, driving training error toward zero, but it now chases noise and does worse on unseen data. Watch test error fall then rise: the sweet spot is the model simple enough to generalise.