The Synthetic Minority Over-sampling Technique, or SMOTE, is a statistical method used to address class imbalance in datasets. In many machine learning applications, one class may have significantly fewer instances than another, leading to biased models. SMOTE generates synthetic examples of the minority class by interpolating between existing instances, effectively increasing its representation in the dataset. By creating new, synthetic data points, SMOTE helps improve the performance of machine learning algorithms, making them more robust and accurate. This technique is particularly useful in fields like medical diagnosis and fraud detection, where minority classes are often critical but underrepresented.