Correct Answer: Class Weighting.

Class Weighting is a technique in Databricks MLlib designed to tackle the issue of imbalanced classes by assigning varying weights to different classes in the training dataset. This approach emphasizes the minority class, enabling the machine learning model to more effectively manage imbalanced class distributions during the training phase. Consequently, this improves the model's accuracy in classifying instances from the minority class. While Feature Scaling, Data Augmentation, and Outlier Detection are valuable techniques in their respective areas, Class Weighting specifically targets the challenge of imbalanced classes, making it the optimal choice for enhancing model performance on such datasets.