Undersampling techniques are methods used in data processing to address class imbalance in datasets, where one class has significantly more instances than another. By reducing the number of instances in the majority class, these techniques help create a more balanced dataset, which can improve the performance of machine learning models. Common undersampling methods include random undersampling, where instances are randomly removed from the majority class, and informed undersampling, which selects instances based on specific criteria. These techniques are essential in fields like machine learning and data science, where balanced datasets lead to more accurate predictions and better model generalization.