Feature engineering is a crucial step in the process of developing machine learning models. It refers to the process of selecting, transforming, and creating new features (input variables or attributes) from the raw data to improve the performance of a machine learning algorithm. The goal of feature engineering is to make the data more suitable for the algorithm by highlighting important information and removing noise or irrelevant information.