SMOTE, or Synthetic Minority Over-sampling Technique, is a statistical method used to address class imbalance in datasets. It works by creating synthetic examples of the minority class, which helps improve the performance of machine learning models. This technique is particularly useful in scenarios where one class is significantly underrepresented compared to another. The process involves selecting a minority class instance and generating new samples by interpolating between it and its nearest neighbors. By doing so, SMOTE enhances the diversity of the training data, allowing algorithms to learn better and make more accurate predictions, especially in fields like medical diagnosis and fraud detection.