Synthetic Minority Over-sampling Technique (SMOTE) is a method used to address class imbalance in datasets by generating synthetic samples for the minority class. It works by interpolating between existing minority class instances to create new, similar instances, thereby improving the performance of machine learning models on imbalanced data.