The practice of altering or developing data features to improve the performance of machine learning models is known as feature engineering. It accomplishes important tasks like enhancing model interpretability, reducing dimensionality, handling non-numeric data, enabling better generalization, utilizing domain expertise, handling missing data, capturing complex relationships, normalizing data, and handling time-series data. In conclusion, it enhances data for machine learning, resulting in models that are more effective and efficient.