Occurs when model complexity (too many parameters) allows memorization of training examples rather than learning general patterns. Symptoms include high training accuracy but low validation/test performance. Governance practices involve regular cross-validation, monitoring train/validation loss divergence, applying regularization techniques (dropout, weight decay), and defining acceptable generalization gaps before allowing models to enter production.