AIC BIC Mallows Cp Cross Validation Model Selection If you have several linear models, say model1, model2 and model3, how would you cross-validate it to pick the best model? 
(In R)
I'm wondering this because my AIC and BIC for each model are not helping me determine a good model. Here are the results: 
Model - Size (including response) - Mallows Cp - AIC - BIC 
Intercept only - 1 - 2860.15 - 2101.61 - 2205.77
1 - 5 - 245.51 - 1482.14 - 1502.97 
2 - 6 - 231.10 - 1472.88 - 1497.87  
3 - 7 - 179.76 - 1436.29 - 1465.45  
4 - 8 - 161.05 - 1422.43 - 1455.75  
5 - 9 - 85.77 - 1360.06 - 1397.55  
6 - 10 - 79.67 - 1354.79 - 1396.44 
7 - 17 - 27.00 - 1304.23 - 1375.04
All Variables - 25 - 37.92 - 1314.94 - 1419.07

Note - assume the models are nested. 
 A: The difference in AIC between two specified models is the estimated expected difference in Kullback–Leibler divergence from each to the true model, & therefore a useful model-selection criterion. The difference in AIC between the model with the lowest AIC out of several & the one with the second-lowest is something else, & therefore has to be taken with a pinch of salt as a model-selection criterion. If 'several' becomes 'many' it's not much use at all. As @gung says here, "if you run a study several times and fit the same model, the AIC will bounce around just like everything else".
What you need to cross-validate is the fit of the final model, repeating the entire model selection procedure within each cross-validation fold. You mention in a comment that you used "Subsets" to create six models, which suggests some selection going on there. Even if you use a measure of fit calibrated by cross-validation instead of AIC to pick the best model, you still need to cross-validate that procedure (see @Dikran's answer here, or @Bogdanovist's here).
