Data imbalance techniques are methods used to address situations where one class of data significantly outnumbers another in a dataset. This imbalance can lead to biased models that perform poorly on the minority class. Common techniques include oversampling, where more instances of the minority class are created, and undersampling, where some instances of the majority class are removed to balance the dataset. Another approach is synthetic data generation, which involves creating new, artificial data points for the minority class using algorithms like SMOTE (Synthetic Minority Over-sampling Technique). These techniques help improve the performance of machine learning models by ensuring they learn effectively from all classes in the dataset.