Is cross validation a tool for developing performance metrics or final models? If you cannot acquire a workable test data-set to test a model, you can use cross validation as an alternative to validate the model. 
However, I'm unsure of the true end purpose of cross validation...
Is CV useful for simply generating a performance metric by providing a way to test your model? Or is it a means to creating a final model?
Put differently: do I use my "best" model from whichever given fold of the CV process (i.e., the "best" model using some partition of the data) as my final model? Or do I perform CV, report my performance metric, and then re-create the model using ALL of my available data to create a final model? 
 A: Is CV useful for simply generating a performance metric by providing a way to test your model? Or is it a means to creating a final model?
For both. It generates performance metrics to test your model (perhaps, on different parameters).
It is also a means of creating the final model, because you would "often" use cross-validation to choose the best parameters of the model. You then use the best parameters on all of the data to generate the final model. However, while reporting please report the cross-validation error.
A: From Zhang 1997:

The purpose of data splitting is to choose
  the best model or to study model stability and predictive
  performance. The final estimation of the model parameters
  should come from the entire data set (Myers, 1990; Shao,
  1993).

Myers RH. 1990. Classical and modern regression with applications. 2nd
Edn. Boston: PWS-KENT Publishing Company.
Shao J. 1993. Linear model selection by cross-validation. Journal of
American Statistical Association 88: 486±494.
ZHANG, LIANJUN. 1997. Cross-validation of non-linear growth functions for modelling tree height–diameter relationships. Annals of Botany 79.3: 251-257.
