Cross Validation on the Test Data

I have split my training data into 5 sets. I am using a basic linear model with all of my predictive variables (because I only have a handful). I repeatedly, manually, set up 5 linear models that train-on-4-of-the-sets, test-on-the-5th-set. I grabbed the RMSE and the Accuracy Percentage for prediction within each of the 5 models.
My question is - what do I do with this information? If I run this exact model on the full 70%training set, then run the predictions on the original 30%test set in order to determine my accuracy, what is the point of the cross validation?

Note: I am running many, many, many models- BoxCox, RandomForest, etc, so I need to be able to compare the prediction abilities of the models- hence the reason that I feel as though I need to run every model again the original 30%test set- for sake of comparison.

Example in R:

#manual cross validation
CV1 <- lm(y1 ~ x1+x2+x3+x4, data=TrainData1)
CV1Predictions <- predict(CV1, TestData1)
mean(CV1Predictions==TestData1$y1) #accuracy measure CV2 <- lm(y1 ~ x1+x2+x3+x4, data=TrainData2) CV2Predictions <- predict(CV2, TestData2) mean(CV2Predictions==TestData2$y1)
.
.
. #this continues for 5 sets


(1) for the lm model above, there is no utility in doing both forms of internal validation. Just choose one, realizing that either form of internal validation has its own limitations.
(2) For machine learning algorithms with hyper-parameters, both forms of validation might be used. So in your example cross-validation may be used to choose mtry value for the randomForest model and then split sample validation could be used to estimate model accuracy. The cross-validation and split-sample are being used with different objectives in mind.