Meta has developed a "Global Feature Importance" approach to enhance feature selection in machine learning by aggregating feature importance scores from multiple models. This method allows for systematic exploration and selection of features, addressing challenges of isolated assessments and improving model performance significantly. The framework supports data engineers and ML engineers in making informed decisions about feature utilization across various contexts, resulting in better predictive outcomes.