Regularization is a widespread technique in machine learning, which is used to control the complexity of the machine learning model and thereby improve its generalization ability.
The bias-variance tradeoff is an important concept in machine learning, which represents the tension that a model has between its ability to reduce the errors on the training set (its bias) versus its ability to generalize well to new unseen examples (its variance).