Feature Engineering is the process of using domain knowledge to extract features from raw data that make machine learning algorithms work more effectively. It involves transforming and selecting the right variables to improve model performance, which can significantly impact the accuracy of predictions. By creating new features or modifying existing ones, data scientists can enhance the learning process of algorithms. Techniques such as normalization, encoding categorical variables, and handling missing values are common practices in feature engineering. This crucial step helps in building robust models that generalize well to unseen data.