Due to the ever-increasing security breaches, practitioners are motivated to produce more secure software. In the United States, the White House Office released a memorandum on Executive Order (EO) 14028 that mandates organizations provide self-attestation of the use of secure software development practices. The OpenSSF Scorecard project allows practitioners to measure the use of software security practices automatically. However, little research has been done to determine whether the use of security practices improves package security, particularly which security practices have the biggest impact on security outcomes. The goal of this study is to assist practitioners and researchers making informed decisions on which security practices to adopt through the development of models between software security practice scores and security vulnerability counts. To that end, we developed five supervised machine learning models for npm and PyPI packages using the OpenSSF Scorecared security practices scores and aggregate security scores as predictors and the number of externally-reported vulnerabilities as a target variable. Our models found four security practices (Maintained, Code Review, Branch Protection, and Security Policy) were the most important practices influencing vulnerability count. However, we had low R^2 (ranging from 9% to 12%) when we tested the models to predict vulnerability counts. Additionally, we observed that the number of reported vulnerabilities increased rather than reduced as the aggregate security score of the packages increased. Both findings indicate that additional factors may influence the package vulnerability count. We suggest that vulnerability count and security score data be refined such that these measures may be used to provide actionable guidance on security practices.