Structural Risk Minimization (SRM) is a principle in statistical learning theory that aims to balance the trade-off between model complexity and training error to achieve better generalization on unseen data. It involves selecting a model from a nested sequence of hypothesis spaces by minimizing an upper bound on the expected risk, thus controlling overfitting and underfitting.