# What are the diagnostic measures for linear regressions?

I am working with the BostonHousing dataset. I have created a number of models and I'd like to select amongst them.

#Initialize the data
library('mlbench')
data(BostonHousing)

fit3 <- lm(medv~ lstat, data = Boston_Ready)
fit4 <- lm(medv~ lstat+rm, data = Boston_Ready)
fit5 <- lm(medv~ lstat+rm+ptratio, data = Boston_Ready)
fit6 <- lm(medv~., data = Boston_Transformed)
fit7 <- lm(medv~., data = Boston)
fit8 <- lm(medv~. -nox -rad -dis, data = Boston)


summary of the models

fit1 uses all the predictors; fit2 uses all the predictors, minus rows with outliers;

fit3 uses the model identified by best subset selection as having lowest BIC; fit4 is the next best BIC; fit5 is the next best BIC after that;

fit6 models the predictors after they have been transformed to have a more normal distribution; fit7 uses all the original, unstandardized data;

fit8 uses all the original data, minus variables with high VIF scores

Question

I can approximate test error with 10-fold, 5-fold, LOOCV and validation set test error. I have calculated those values for each of the models.

Is that enough for choosing a model? If my goal is prediction accuracy, then can I simply choose the model with the lowest test error, MSE, from my cross-validation?

Follow-up. I hope it's alright. Can I use BIC for model selection?