The advent of machine learning and data science has revolutionized investment practice, especially quantitative investing. Practitioners now have at their disposal an array of traditional and cutting edge statistical tools, and while such a plethora of choices may seem to some to be a godsend, to others it presents a "paralysis of choice" where it is difficult to know which tool to use for a given application. Given this state-of-affairs, in this article we discuss the origins, characteristics, and appropriate application of econometrics and machine learning, and provide a general methodology for their individual or joint employment in investment applications.