The brief answer is random sampling, but the more difficult issue is determining the size of the random sample that you should use. One efficient solution to that problem is provided by progressive sampling—a method that Foster Provost, Tim Oates, and I developed in the late 1990s . The approach begins with a small sample size and increases sample size according to a sampling schedule, checking whether model accuracy increases at each iteration. We show that a geometric schedule (e.g., doubling the sample size on each iteration) is asymptotically no worse than knowing the correct sample size in advance.
 F. Provost, D. Jensen, and T. Oates (1999). Efficient progressive sampling. Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.