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Feature selection is important in machine learning primarily because it improves model interpretability and performance. By selecting only the most relevant features from the dataset, one can simplify the model, making it easier to understand and interpret. When irrelevant or redundant features are removed from the dataset, the model can focus on the most significant variables that contribute to the predictive power. This not only enhances the model's accuracy but also speeds up the training process, as fewer features mean less computational complexity.

Moreover, reducing the number of features can help mitigate issues such as the curse of dimensionality, where the performance of the model is compromised due to the high dimensionality of the data. In addition to improved performance, a model with fewer, more meaningful features is generally easier to communicate and justify to stakeholders, which is essential in many practical applications.

While it is true that feature selection can help reduce the amount of data processed and may help prevent overfitting, those aspects are secondary to the primary benefits of enhancing interpretability and performance. Ensuring that all features contribute equally to the model is not typically a goal of feature selection; rather, the aim is to identify and retain only those features that provide useful information for the prediction task.