Information access systems have supported people during tasks across a variety of domains. In this perspective paper, we advocate for broadening the scope of information access research to include machines. We believe that machine learning can be substantially advanced by developing a research program around retrieval as a core algorithmic method. This paper describes how core principles of indexing, representation, retrieval, and relevance can extend supervised learning algorithms. It proposes a generic retrieval-enhanced machine learning (REML) framework and describes challenges in and opportunities introduced by implementing REML. We also discuss different optimization approaches for training REML models and review a number of case studies that are simplified and special implementations of the proposed framework. The research agenda introduced in this paper will smooth the path towards developing machine learning models with better scalability, sustainability, effectiveness, and interpretability.