Data balancing is a technique used in machine learning to ensure that different classes in a dataset are represented equally. When one class has significantly more samples than another, it can lead to biased models that perform poorly on the underrepresented class. Balancing the data helps improve the model's accuracy and fairness. There are several methods for data balancing, including oversampling, where more instances of the minority class are created, and undersampling, where some instances of the majority class are removed. Other techniques, like SMOTE (Synthetic Minority Over-sampling Technique), generate synthetic samples to enhance the minority class without losing valuable information.