Are K-Fold Cross Validation , Bootstrap ,Out of Bag fundamentally same? Can Anyone tell me how K-Fold Cross Validation ,Bootstrap and Out of Bag Approach differ as they use
1)Separate data into training data and testing data
2)Make model using training data and prediction using testing data
3)Use Random Sampling with repeatition approach 
4)Repeat the above procedure N times 
5)Average all predicted value to get a single value for each prediction of a response variable y 
 A: Altough these 3 approaches consists in dividing a dataset into several subsets, they are still different in the main purpose of this division.
K-Fold Cross Validation (CV)
It consists in dividing the original set of observations into k subset of more or less same size. Then, you will use one of the subset as test set and the remaining subsets will be used to form your training set. You will repeat this kth times, where each time the subset used as test set will change.
As an example, if you use 3-fold CV, your original set will be divided into k1, k2, k3.
First, k1 will form the test set, k2 and k3 will form the training set.
Then, k2 will form the test set, k1 and k3 will form the training set.
Finally, k3 will form the test set, k1 and k3 will form the training set.
For each fold, you output the results and you aggregate these to obtain the final result.
Bootstrap
A bootstrap is a random subset of your original data, sometimes drawn with replacement (check http://www.stat.washington.edu/courses/stat527/s13/readings/EfronTibshirani_JASA_1997.pdf on the .632 rule), sometimes not. But the idea is that a bootstrap contains only a part of your whole set of observations.  It is different from CV as it does not contain a testing set.
Bootstrap is used to train a different classifier each time on a different set of observations. To output your results, a combination method is used, like averaging for example.
Out-of-bag
As said above, not all observations are used to form bootstrap. The part not used forms the out-of-bag classifier, and can be used to assess the error rate of your classifier. Out-of-bag are typically used to compute the error-rate, and not to train your classifier.
A: First of all, you're right about the similarities: they are all types of resampling-based error estimates. Now about the differences. 
cross validation vs. out-of-bootstrap: cross validation (as well as random splitting procedures known as set validation or hold-out validation) use resampling without replacement whereas bootstrap procedures resample with replacement. In cross validation, the resampling without replacement is done in a way that ensures each sample is tested exactly once per "run" of the cross validation.
(There are also non-random splitting procedures for cross validation such as venetian blinds or contiguous blocks which are used in special situations.)
Which of these procedures is best for your model validation depends e.g. on your sample situation and on the model you're validating (bootstrap is of no use with a modeling algorithm that deletes duplicates).
Out-of-bag estimates are very different from the "normal" out-of-bootstrap or cross validation estimates, because they do not estimate the generalization error of single models (typically the one model built on the whole data set) but the generalization error of an aggregated (ensemble) model (bag = bootstrap aggregation). So this difference is like the difference between a single decision tree and a random forest.
Again, you can also use resampling without replacement (e.g. cross validation) to generate the model ensemble for the aggregation - the principle works exactly the same. (Ask me if you need a literature example)

@benmaq already pointed out, bootstrapping is often used for other purposes than validation, particularly to estimate variability due to the random process of sampling that lead to the sample at hand.
An analogous procedure with resampling without replacement or more precisely, using the surrogate models of leave-one-out cross validation is known as jackknifing.
