Worked on improving the reliability of core machine learning models in the scikit-learn/scikit-learn repository, focusing on Python-based data science workflows. Addressed a robustness issue in LogisticRegressionCV by resolving a TypeError that occurred when the l1_ratio_ attribute was None, implementing a guard to default this value to 0.0 when appropriate. Added a regression test to ensure the fix remains effective during model fitting and refitting, particularly when legacy attributes are not used. This targeted update enhanced the stability and maintainability of cross-validated logistic regression, reducing potential runtime errors and improving test coverage without introducing feature regressions.