Suppose we have a regression model. If we get estimated of some of the coefficients and the standard errors are high, does this mean that the model is wrong/bad? How exactly do statistical packages choose regression models (in particular ordinal regression)?
The "goodness" or "badness" of a regression model cannot be judged by any set of statistics alone. A model is "good" if it enlightens you, helps you solve a problem.... etc. or, to the extent to which it meets the "Magic" criteria, as introduced by Robert Abelson in his book Statistics as Principled Argument (link goes to my review of the book).
A high standard error (relative to the coefficient) means either that 1) The coefficient is close to 0 or 2) The coefficient is not well estimated or some combination. "High" by itself doesn't really have a set meaning (you can change the SE by changing the unit - measure in miles instead of microns and the SE will be tiny).