1. A machine-based method comprising: in connection with a project, selecting a first subset of historical data about a system being modeled for use in generating a tentative predictive model for the system, the first subset of historical data comprising less than all of the historical data; selecting a process for developing a predictive model for the system from among multiple possible model development processes; applying the selected model development process to the first subset of historical data to generate the tentative predictive model; selecting a second subset of the historical data, the second subset being less than all of the historical data and being at least a portion of a complementary dataset of the first subset or being randomly selected from the historical data and independent of the first subset; applying the tentative predictive model to the selected second subset, determining whether results of applying the tentative predictive model to the selected second subset validate that the selected model development process will produce a final predictive model that is accurate for data that is not part of the historical data, if the selected model development process is so validated, then applying the validated model development process to a full set of historical data that includes the first and second subsets to generate a final predictive model, and using the final predictive model.