Feature transformation is a crucial preprocessing step in machine learning that involves modifying the original features to enhance the model's performance or meet its assumptions. It includes techniques like normalization, scaling, and encoding to ensure that the data is in a suitable format for analysis and improves the algorithm's ability to learn meaningful patterns.