Feature scaling is an essential preprocessing step in machine learning that involves normalizing or standardizing the range of independent variables or features. It ensures that all features contribute equally to the model's learning process, especially when different features are measured in varying units (e.g., age in years vs. income in dollars).

Without feature scaling, algorithms like gradient descent-based models (e.g., linear regression, logistic regression, neural networks) can struggle to converge due to unequal feature magnitudes, leading to longer training times and suboptimal model performance. Distance-based algorithms, such as k-nearest neighbors (KNN) or support vector machines (SVM), are particularly sensitive to feature scales. In these models, features with larger ranges can disproportionately influence distance calculations, skewing results.

Feature scaling helps models converge faster and more effectively, improving accuracy and model generalization. Common techniques include min-max normalization, which scales values to a specific range, and z-score standardization, which rescales features to have a mean of zero and a standard deviation of one.

In summary, feature scaling plays a crucial role in optimizing model performance and efficiency. To deepen your understanding of this topic, enrolling in a machine learning data science course can be highly beneficial.