Bootstrap and cross-validation for evaluating modelling strategies

I've been re-reading Frank Harrell's Regression Modelling Strategies, a must read for anyone who ever fits a regression model, although be prepared - depending on your background, you might get 30 pages in and suddenly become convinced you've been doing nearly everything wrong before, which can be disturbing. I wanted to evaluate three simple modelling strategies in dealing with data with many variables. Using data with 54 variables on 1,785 area units from New Zealand's 2013 census, I'm looking to predict median income on the basis of the other 53 variables. The features are all continuous and are variables like "mean number of bedrooms", "proportion of individuals with no religion" and "proportion of individuals who are smokers". None of these is exactly what I would use for real, but they serve the purpose of setting up a competition of strategies that I can test with a variety of model validation techniques.