Fit polynomials of increasing degree to data and watch the model go from too simple to memorizing noise. Observe how training error and test error diverge when complexity grows unchecked.
Balanced bias-variance, generalizes well
When test error rises while train error falls, the model is memorizing noise instead of learning the pattern. The gap between the two errors measures generalization.