Key objective: To provide an overview of machine learning in oncology, both from a methods and applications perspective, and to offer a framework for leveraging machine learning in clinical decision making. Knowledge generated: This review presents an overview of common machine learning algorithms and clinical data sources and discusses their relative merits. The data curation process is outlined, along with the technical challenges involved in working with large-scale healthcare data. Many aspects of oncology have benefited from these approaches, with applications ranging from early detection to treatment evaluation. Relevance: Machine learning presents an opportunity to transform cancer care through data-driven insights. This review provides practitioners with a practical view of the machine learning pipeline and its challenges.