The sheer number of model evaluation techniques available to asses how good your model is can be completely overwhelming.  As well as the oft-used confidence intervals, confusion matrix and cross validation, there are dozens more that you could use for specific situations, including McNemar's test, Cochran's Q, Multiple Hypothesis testing and many more. This one picture whittles down that list to a dozen or so of the most popular. You'll find links to articles explaining the specific tests and procedures below the image.

More Info & Further Reading

General:

Confidence Intervals

Cross Validation

Chi-Squared

Regression:

RMSE

Correlation and R-Squared for Big Data (RMSE L2 version)

MAE

RAE

R-Squared & Adjusted R-Squared

Classification Visuals:

Understanding And Interpreting Gain And Lift Charts

ROC

KS Charts

Classification Statistics and Tools:

Concordance

F Statistic

Confusion Matrix

Gini Coefficient

Classification: Accuracy

References

11 Important Model Evaluation Techniques Everyone Should Know

Introduction to Machine Learning Model Evaluation
Model Evaluation: Classification

Model Evaluation: Regression Models

What is Predictive Models Performance Evaluation and Why it is Impo...

Classification: Accuracy

Gain and Lift Image: Understanding And Interpreting Gain And Lift Charts