What is the difference between residual sum of squares and ordinary least squares?
Here is a definition from Wikipedia:


  In statistics, the residual sum of squares (RSS) is the sum of the
  squares of residuals. It is a measure of the discrepancy between the
  data and an estimation model; Ordinary least squares (OLS) is a method
  for estimating the unknown parameters in a linear regression model,
  with the goal of minimizing the differences between the observed
  responses in some arbitrary dataset and the responses predicted by the
  linear approximation of the data.


So RSS is a measure of how good the model approximates the data while OLS is a method of constructing a good model.